Abstract

Naturally arising patterns in data are known to present potential sources of useful information at
both micro and macro-economic levels. We carry out unsupervised and supervised modelling of
Botswana's macro-economic data attributes obtained from disparate sources. Both techniques,
commonly used to detect inherent patterns in data, have adjustable parameters which inevitably
vary across applications. Thus, we propose a sequential unsupervised-supervised modelling
approach in which Exploratory Data Analysis (EDA) is used to detect basic structures in data
which are then passed on an algorithm based on the Expectation-Maximisation (EM) mechanics.
The EM convergent values are then used to guide data labelling before applying the neural
networks model. We demonstrate how future economic structures may be detected, monitored
and managed by iteratively focusing on conditional checks of a generic algorithm. For the
purposes of modelling robustness, we propose setting up an integrated data repository and
source that would provide data-based guidelines to policy makers in addressing the country's
economic issues while providing economic researchers access to data and/or information
resources. Outstanding issues are identified and discussed and potential future directions are
clearly highlighted.